自适应VMD的地震资料高分辨率处理方法研究

段成祥; 梁圆; 范晓辉; 张繁昌; 吴俊

doi:10.15953/j.1004-4140.2022.31.02.01

自适应VMD的地震资料高分辨率处理方法研究

doi: 10.15953/j.1004-4140.2022.31.02.01

中国石油大学(华东)地球科学与技术学院, 山东青岛266580

基金项目: 国家自然科学基金面上项目（致密裂隙介质波致流机理及物性甜点检测关键算法研究（41874146））

详细信息

作者简介:
段成祥：男，中国石油大学（华东）地质工程专业硕士研究生，主要从事地震资料处理及解释研究，E-mail：duancx2021@126.com

中图分类号: P 315；P 631；O 242
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- 被引次数: 0
出版历程
- 收稿日期: 2021-01-07
- 网络出版日期: 2022-01-25
- 刊出日期: 2022-04-01

Research on High Resolution Seismic Date Processing Method Based on Adaptive VMD

School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China

摘要

摘要: 随着勘探开发的不断深入，常规地震资料受分辨率的限制难以满足精细勘探开发的需求。由于地震信号不同频率成分的衰减程度不同，故可结合分频技术对各频率成分进行差异化补偿，进而提高地震资料分辨率。而常规分频技术普遍分频精度不高，存在模态混叠现象，不能较好地适用于地震资料处理。针对上述问题，本文提出基于自适应变分模态分解（VMD）的地震资料高分辨率处理方法。将多目标蝙蝠算法应用于变分模态分解，利用功率谱熵、能量差、样本熵构建适应度函数，对VMD参数进行优化。模型测试结果表明，优化的VMD方法分频精度较高，避免模态混叠，且具有较强的抗噪能力；将优化VMD方法应用于地震资料高分辨率处理，模型及实际数据测试结果表明，处理后的地震资料分辨率得到有效提高。
- 粘弹性介质 /
- 变分模态分解 /
- 多目标蝙蝠算法 /
- 参数优化 /
- 高分辨率处理
Abstract: With the deepening of exploration and development, due to the limitation of the resolution of the conventional seismic data, it is difficult to meet the needs of exploration and development. Since the attenuation degree of different frequency components of seismic signals is different, the frequency decomposing technology can be applied to perform differential compensation on each frequency component to improve the resolution of seismic data. However, the conventional frequency division technology generally not only holds low frequency division accuracy but also shows modal aliasing, thus it cannot be well applied to seismic data processing. To solve these problems, this paper proposes a high-resolution processing method for seismic data based on adaptive variational modal decomposition (VMD). The multi-objective bat algorithm is applied to the variational modal decomposition, and the VMD parameters are optimized by the fitness function constructed using power spectrum entropy, energy difference, and sample entropy. The model test results show that the optimized VMD method holds high frequency division accuracy and strong anti-noise ability, and also can avoids modal aliasing, When the optimized VMD method is applied to high-resolution processing of seismic data, the model and actual data test results show that the resolution of processed seismic data is effectively improved.
- viscoelastic media /
- variational modal decomposition /
- multi-objective bat algorithm /
- parameter optimization /
- high resolution processing

HTML全文

图 1 合成信号及频谱图

Figure 1. Synthetic signal and amplitude spectrum

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图 2 适应度值随迭代次数变化曲线

Figure 2. Curve of fitness value with the number of iterations

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图 3 信号$ y $的VMD分解及重构结果

Figure 3. VMD decomposition and reconstruction results of signaly

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图 4 合成地震记录

Figure 4. Synthetic seismic record

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图 5 经VMD高频补偿前后信号对比

Figure 5. Signal comparison before and after VMD high frequency compensation

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图 6 经VMD高频补偿前后信号振幅谱对比

Figure 6. Comparison of signal amplitude spectrum before and after VMD high frequency compensation

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图 7 原始地震数据

Figure 7. Original seismic data

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图 8 反褶积方法处理结果

Figure 8. Deconvolution processing result

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图 9 本文方法高频补偿后结果

Figure 9. The result of this method after high frequency compensation

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图 10 本方法处理前后频谱对比

Figure 10. Spectrum comparison before and after processing by this method

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表 1 优化VMD分解精度评价指标

Table 1. Optimize the evaluation index of VMD decomposition accuracy

对应分量相关系数标准误差

IMF1-y1 0.9804 0.1025
IMF2-y2 0.9838 0.0904
IMF3-y3 0.9831 0.1302
Fig.3（i）-Fig.1（g） 0.9951 0.0512

下载: 导出CSV

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